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Capsules for Biomedical Image Segmentation [article]

Rodney LaLonde, Ziyue Xu, Ismail Irmakci, Sanjay Jain, Ulas Bagci
<span title="2020-12-10">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We extend the masked reconstruction regularization to the task of segmentation and perform thorough ablation experiments on each component of our method.  ...  The proposed convolutional-deconvolutional capsule network, SegCaps, shows state-of-the-art results while using a fraction of the parameters of popular segmentation networks.  ...  Acknowledgments This study is partially supported by the NIH grant R01-EB020539.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.04736v2">arXiv:2004.04736v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zbofj34mnzcotf5dkpbwoocrda">fatcat:zbofj34mnzcotf5dkpbwoocrda</a> </span>
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3DCapsule: Extending the Capsule Architecture to Classify 3D Point Clouds [article]

Ali Cheraghian, Lars Petersson
<span title="2018-11-06">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Similarly, the ComposeCaps layer is evaluated and demonstrates an improvement over the baseline.  ...  The original Capsule relies on the existence of a spatial relationship between the elements in the feature map it is presented with, whereas in point permutation invariant formulations of 3D point set  ...  cloud should be projected on like the view-based methods do.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1811.02191v1">arXiv:1811.02191v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/e3ir2wmkpjd55iqlapdfm3ox3q">fatcat:e3ir2wmkpjd55iqlapdfm3ox3q</a> </span>
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Learning Capsules for SAR Target Recognition

Yunrui Guo, Zongxu Pan, Meiming Wang, Ji Wang, Wenjing Yang
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b2n2tpw5ang73osulebz6bm4ju" style="color: black;">IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</a> </i> &nbsp;
This article proposes a novel method, SAR capsule network, to achieve the accurate and robust classification of SAR images without significantly increasing network complexity.  ...  The SAR capsules are learned by a vector-based full connection operation instead of the traditional routing process, which not only alleviates the computational burden but also improves recognition accuracy  ...  Configuration parameters of SAR capsule network (left) and the reconstruction network (right). Fig. 4 . 4 Fig. 4.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jstars.2020.3015909">doi:10.1109/jstars.2020.3015909</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7vkzaezjzncv5cypw2duew33ia">fatcat:7vkzaezjzncv5cypw2duew33ia</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210717150850/https://ieeexplore.ieee.org/ielx7/4609443/8994817/09164999.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/00/fc/00fcf72e5e61f4ac68056d545191dfe7b74cec81.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jstars.2020.3015909"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Brain Tumor Type Classification via Capsule Networks [article]

Parnian Afshar, Arash Mohammadi, Konstantinos N. Plataniotis
<span title="2018-03-01">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we focus to achieve the following four objectives: (i) Adopt and incorporate CapsNets for the problem of brain tumor classification to design an improved architecture which maximizes the  ...  Brain tumor is considered as one of the deadliest and most common form of cancer both in children and in adults.  ...  and reconstructed ones.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1802.10200v2">arXiv:1802.10200v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/i6ij6aosy5bdtn3mweaimkpmhm">fatcat:i6ij6aosy5bdtn3mweaimkpmhm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191016023011/https://arxiv.org/pdf/1802.10200v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c8/70/c870432c2f0ca7b18427784ee5896fa993261d38.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1802.10200v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

CapSurv: Capsule Network for Survival Analysis with Whole Slide Pathological Images

Bo Tang, Ao Li, Bin Li, Minghui Wang
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Currently, survival analysis based on pathological images has turned out to be a truly energetic area in the research of healthcare for making primary decisions on therapy and improving patients' quality  ...  Our method is applied to the predictions of the survival of glioblastoma and lung squamous cell carcinoma with a public cancer dataset.  ...  It's on the basis of the newly proposed capsule network and improved for survival analysis by proposing a novel loss function named survival loss.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2901049">doi:10.1109/access.2019.2901049</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pjrxq75keva5lhbddtuck2ujca">fatcat:pjrxq75keva5lhbddtuck2ujca</a> </span>
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SECaps: A Sequence Enhanced Capsule Model for Charge Prediction [article]

Congqing He, Li Peng, Yuquan Le, Jiawei He
<span title="2018-10-10">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Automatic charge pre-diction plays an important role in assisting judges and lawyers to improve the effi-ciency of legal decisions, and thus has received much attention.  ...  On the other hand, some works have shown the benefits of capsule net-work, which is a powerful technique.  ...  Compared to this work, our work shares several common features with they: (1) we are both based on deep neural network methods (2) we are both trying to solve the few-shot problem of charge prediction.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1810.04465v1">arXiv:1810.04465v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pbtwqaqh5fclxkamsgqdzcqanq">fatcat:pbtwqaqh5fclxkamsgqdzcqanq</a> </span>
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Group Equivariant Capsule Networks [article]

Jan Eric Lenssen, Matthias Fey, Pascal Libuschewski
<span title="2018-10-24">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Through this connection, we provide intuitions of how both methods relate and are able to combine the strengths of both approaches in one deep neural network architecture.  ...  Second, we connect the resulting equivariant capsule networks with work from the field of group convolutional networks.  ...  Acknowledgments Part of the work on this paper has been supported by Deutsche Forschungsgemeinschaft (DFG) within the Collaborative Research Center SFB 876 Providing Information by Resource-Constrained  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1806.05086v2">arXiv:1806.05086v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wyruvr45frbrpnypxpbmzosjyq">fatcat:wyruvr45frbrpnypxpbmzosjyq</a> </span>
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Deep variational network for rapid 4D flow MRI reconstruction

Valery Vishnevskiy, Jonas Walheim, Sebastian Kozerke
<span title="2020-04-13">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/v66j35cgxvajrnw3y4tkpw4ine" style="color: black;">Nature Machine Intelligence</a> </i> &nbsp;
We propose an efficient model-based deep neural reconstruction network and evaluate its performance on clinical aortic flow data.  ...  Remarkably, the relatively low amounts of tunable parameters allowed the network to be trained on images from 11 reference scans while generalizing well to retrospective and prospective undersampled data  ...  Acknowledgements The authors acknowledge funding from the European Unions Horizon 2020 research and innovation program under grant agreement No 668039 and under EuroStars UNIFORM as well as funding of  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s42256-020-0165-6">doi:10.1038/s42256-020-0165-6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/c6i6tjgrsfb2fp2yziegfoutae">fatcat:c6i6tjgrsfb2fp2yziegfoutae</a> </span>
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Point2SpatialCapsule: Aggregating Features and Spatial Relationships of Local Regions on Point Clouds using Spatial-aware Capsules [article]

Xin Wen, Zhizhong Han, Xinhai Liu, Yu-Shen Liu
<span title="2019-08-29">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Compared to the previous capsule network based methods, the feature routing on the spatial-aware capsules can learn more discriminative spatial relationships among local regions for point clouds, which  ...  To address this issue, we propose a novel deep learning network, named Point2SpatialCapsule, for aggregating features and spatial relationships of local regions on point clouds, which aims to learn more  ...  Since most of the methods focusing on 3D shape retrieval are based on multi-views of 3D models, in this subsection, we also quote the experimental results of the multi-view based methods to verify the  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1908.11026v1">arXiv:1908.11026v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gekv432wgrcybfskhhgmz766pm">fatcat:gekv432wgrcybfskhhgmz766pm</a> </span>
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Improving the Similarity Measure of Determinantal Point Processes for Extractive Multi-Document Summarization

Sangwoo Cho, Logan Lebanoff, Hassan Foroosh, Fei Liu
<span title="">2019</span> <i title="Association for Computational Linguistics"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/5n6volmnonf5tn6xputi5f2t3e" style="color: black;">Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</a> </i> &nbsp;
The approach measures redundancy between a pair of sentences based on surface form and semantic information.  ...  In this paper we seek to strengthen a DPP-based method for extractive multi-document summarization by presenting a novel similarity measure inspired by capsule networks.  ...  Acknowledgments The authors are grateful to the reviewers for their insightful feedback. We would also like to extend our thanks to Boqing Gong, Xiaodan Zhu and Fei Sha for useful discussions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18653/v1/p19-1098">doi:10.18653/v1/p19-1098</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/acl/ChoLFL19.html">dblp:conf/acl/ChoLFL19</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/v4q2comewraivnqpytde3hmyze">fatcat:v4q2comewraivnqpytde3hmyze</a> </span>
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CBIR System Using Capsule Networks and 3D CNN for Alzheimer's Disease Diagnosis

K.R. Kruthika, Rajeswari, H.D. Maheshappa
<span title="">2019</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vusydvbzgfck3ik2qu2c5h4zea" style="color: black;">Informatics in Medicine Unlocked</a> </i> &nbsp;
It was observed that an ensemble method using 3D-CapsNets and a convolutional neural network (CNN) with 3D-autoencoder, increased the detection performance comparing to Deep-CNN alone.  ...  Alzheimer's disease (AD) is an irreversible disorder of the brain related to loss of memory, commonly seen in the elderly and aging population.  ...  Acknowledgements Data collection and sharing for this project was funded by the AD Neuroimaging Initiative (ADNI), National Institutes of Health Grant U01 AG024904 and DOD (Department of Defense award  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.imu.2019.100227">doi:10.1016/j.imu.2019.100227</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jlxfsbxkdbhhfhyuthlbs36ycq">fatcat:jlxfsbxkdbhhfhyuthlbs36ycq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191115134918/https://pdf.sciencedirectassets.com/312075/AIP/1-s2.0-S235291481930228X/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEDYaCXVzLWVhc3QtMSJGMEQCIGWdcNtskqJZwr1%2B7CgynBO%2Bzevx1GAweyQObk6pYf%2FUAiBgKJuT%2B4WTL8l2pPNNHDNfq9okJyBVc2X7Pr6WMNLFaCrQAgheEAIaDDA1OTAwMzU0Njg2NSIMc59uSPXhbvFS0JVnKq0CiK3TG2HOL6b03HijGRLH500IwpAOexb1mC5Axrs5iz8WuLwwdk3WCKsn9prQH%2FfpQ1YgzpnqoDBhFlVBDE%2BPzYEknJcFruj8qTNpOnVaWF2vv3dzgd4pmm%2FuIdvOy8JsCaS0YTcjAIMmEqkhxUDA3PNgYrBZEySJZCfw%2FdF5aUeWCsaEgznxR4VyJgx207R3Vc%2BwEAMsoHdN9MiaFeN9FfMoOYKwSfCbjSmmHZjAReL0zZPWU7G8zBhmq%2BerL64J0GeTfmlHo1gJDRu70R19xArZaARJHAkISXA%2BQbLwuOh0KNRSbICQEQUF59QuOPKEbtIOfennd8muYxZ8zeR32Z5bmtdkAWTtIYu%2BgevOCBfp8VmpGQaXPeRMJkG8LyUSMogLIIqff3IVyvqi8TDaxLruBTrQAo%2FfbkAwGMP7hWja0ELyqWypizV76JHvMtZ9EPBo%2FxLkP%2BZbh%2BORwK836%2Fv14INCspo835Ccr37yTaW1s26Ex9hTCmZLm5zJ6detjV%2FGsk14V2AuXCB9jrGS%2FXcis4NrahHJUTYhM%2F8fAzKXOpr4b0niUcufw47WCzBR9xIBirj%2BI7pVeSC1v04q3jD65SYjJFeI9oLzQqHuLV%2FYmP9kF7DiQqnLyNbs7gc3rX6ssTSoeH3jmiaQb6hhWwhkhPH6xZSTLyfHqWU13p0d62BkS6h8bTDmLZAyYVABJQn9IKSgMptZRYu1ah5P5Oj%2Bd5Gaa%2FsG6VkUX3CTMB39vFkVH4AfmMaRxgwLiX1309ufhqUJyG%2ByJhkffKEF%2Fo6oh1c0N1cEjL3APYCEPDqM8%2FsUxuXa25TW7PBIhsbRCHX4gbWAXAv%2FhXgPRia0tHnfHmXZLw%3D%3D&amp;X-Amz-Algorithm=AWS4-HMAC-SHA256&amp;X-Amz-Date=20191115T134917Z&amp;X-Amz-SignedHeaders=host&amp;X-Amz-Expires=300&amp;X-Amz-Credential=ASIAQ3PHCVTYQ3JOD45X%2F20191115%2Fus-east-1%2Fs3%2Faws4_request&amp;X-Amz-Signature=1d333fc18c534c8e7af033825c48d6414dbf8b70909bfbc3343e16092b886928&amp;hash=4ae90c848e68715aba6680ba02a04cd6b9fa37afa8683e9ee70a8deda2615868&amp;host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&amp;pii=S235291481930228X&amp;tid=spdf-e9570ca5-d8c3-499a-baac-1a03bfef4d97&amp;sid=614d1a438d5db-45f2-91f4-6f317165f3d8gxrqa&amp;type=client" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ec/2f/ec2f3cb25078d38748b48bbebc3be39a99110405.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.imu.2019.100227"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> elsevier.com </button> </a>

Capsule Networks for Brain Tumor Classification based on MRI Images and Course Tumor Boundaries [article]

Parnian Afshar, Konstantinos N. Plataniotis, Arash Mohammadi
<span title="2018-11-01">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Determining the type of brain tumor has significant impact on the treatment choice and patient's survival.  ...  Among different types of cancer, brain tumor is seen as one of the deadliest forms due to its aggressive nature, heterogeneous characteristics, and low relative survival rate.  ...  The original Capsule network has also a set of fully connected layers, referred to as the decoder part, that takes the final instantiation parameters of the true classes as inputs, and try to reconstruct  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1811.00597v1">arXiv:1811.00597v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wg67z6dmp5csjf6s4ooxzzy75y">fatcat:wg67z6dmp5csjf6s4ooxzzy75y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200910051419/https://arxiv.org/pdf/1811.00597v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/99/f6/99f6bfa6182fd497554f5e6be1ea27f30f8cc2c9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1811.00597v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

SE-CapsNet: Automated evaluation of plant disease severity based on feature extraction through Squeeze and Excitation (SE) networks and Capsule networks

Shradha Verma, University School of Information, Communication & Technology (USIC&T), Guru Gobind Singh Indraprastha University (GGSIPU), New Delhi, India., Anuradha Chug, Ravinder P. Singh, Amit P. Singh, Dinesh Singh, University School of Information, Communication & Technology (USIC&T), Guru Gobind Singh Indraprastha University (GGSIPU), New Delhi, India., Division of Plant Pathology, Indian Agricultural Research Institute (IARI), New Delhi, India., University School of Information, Communication & Technology (USIC&T), Guru Gobind Singh Indraprastha University (GGSIPU), New Delhi, India., Division of Plant Pathology, Indian Agricultural Research Institute (IARI), New Delhi, India.
<span title="2021-12-02">2021</span> <i title="Kuwait Journal of Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7r7osjshkjholmpcusnobdus5e" style="color: black;">Maǧallaẗ Al-Kuwayt li-l-ʿulūm</a> </i> &nbsp;
In this paper, the authors have proposed an improved feature computation approach based on Squeeze and Excitation (SE) Networks, before processing by the original Capsule networks (CapsNet) for classification  ...  Two SE networks, one based on AlexNet and another on ResNet have been combined with Capsule networks.  ...  The authors aim to collect images via digital and hyperspectral cameras of tomato crop, in laboratory as well as field conditions, for further research and explore the possibilities of early detection  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.48129/kjs.v49i1.10586">doi:10.48129/kjs.v49i1.10586</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7lw3ulchunaxdkktwwjtba6nzu">fatcat:7lw3ulchunaxdkktwwjtba6nzu</a> </span>
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Improved anomaly detection by training an autoencoder with skip connections on images corrupted with Stain-shaped noise [article]

Anne-Sophie Collin, Christophe De Vleeschouwer
<span title="2020-11-04">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To improve the sharpness of the reconstruction, we consider an autoencoder architecture with skip connections.  ...  In addition to demonstrating the relevance of our approach, our validation provides the first consistent assessment of reconstruction-based methods, by comparing their performance over the MVTec AD dataset  ...  Conventional reconstruction-based methods infer anomaly based on the reconstruction error between an arbitrary input and its reconstructed version.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.12977v2">arXiv:2008.12977v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dqfdyzvyzjegnb7vv3ngutpera">fatcat:dqfdyzvyzjegnb7vv3ngutpera</a> </span>
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From Hand-Crafted to Deep Learning-based Cancer Radiomics: Challenges and Opportunities [article]

Parnian Afshar, Arash Mohammadi, Konstantinos N. Plataniotis, Anastasia Oikonomou, Habib Benali
<span title="2019-02-20">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Considering the variety of approaches to Radiomics, further improvements require a comprehensive and integrated sketch, which is the goal of this article.  ...  Radiomics is an emerging and relatively new research field, which refers to extracting semi-quantitative and/or quantitative features from medical images with the goal of developing predictive and/or prognostic  ...  The interpretability of meaningless weights is improved in the newly proposed Capsule networks through reconstructing the input image based on the features learned by the network.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1808.07954v3">arXiv:1808.07954v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/huc23wcklfey5aetnlbe6o4h34">fatcat:huc23wcklfey5aetnlbe6o4h34</a> </span>
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